Hospital-acquired infections (HAIs) pose a serious threat to patients, and hospitals spend billions of dollars each year to reduce and treat these infections. Many HAIs are due to contamination from workers’ hands and contact with high-touch surfaces. Therefore, we set out to test the efficacy of a new preventative technology, AIONX® Antimicrobial Technologies, Inc’s cleanSURFACES®, which is designed to complement daily chemical cleaning events by continuously preventing re-colonization of surfaces. To that end, we swabbed surfaces before (Baseline) and after (Post) application of the cleanSURFACES® at various time points (Day 1, Day 7, Day 14, and Day 28). To circumvent limitations associated with culture-based and 16S rRNA gene amplicon sequencing methodologies, these surface swabs were processed using metatranscriptomic (RNA) analysis to allow for comprehensive taxonomic resolution and the detection of active microorganisms. Overall, there was a significant (P < 0.05) global reduction of microbial diversity in Post-intervention samples. Additionally, Post sample microbial communities clustered together much more closely than Baseline samples based on pairwise distances calculated with the weighted Jaccard distance metric, suggesting a defined shift after product application. This shift was characterized by a general depletion of several microbes among Post samples, with multiple phyla also being reduced over the duration of the study. Notably, specific clinically relevant microbes, including Staphylococcus aureus, Clostridioides difficile and Streptococcus spp., were depleted Post-intervention. Taken together, these findings suggest that chemical cleaning events used jointly with cleanSURFACES® have the potential to reduce colonization of surfaces by a wide variety of microbes, including many clinically relevant pathogens.

Arsenic is ubiquitous in nature, highly toxic, and is particularly abundant in Southern Asia. While many studies have focused on areas like Bangladesh and West Bengal, India, disadvantaged regions within Nepal have also suffered from arsenic contamination levels, with wells and other water sources possessing arsenic contamination over the recommended WHO and EPA limit of 10 μg/L, some wells reporting levels as high as 500 μg/L. Despite the region's pronounced arsenic concentrations within community water sources, few investigations have been conducted to understand the impact of arsenic contamination on host gut microbiota health. This study aims to examine differential arsenic exposure on the gut microbiome structure within two disadvantaged communities in southern Nepal. Fecal samples (n ¼ 42) were collected from members of the Mahuawa (n ¼ 20) and Ghanashyampur (n ¼ 22) communities in southern Nepal. The 16S rRNA gene was amplified from fecal samples using Illumina-tag PCR and subject to high-throughput sequencing to generate the bacterial community structure of each sample. Bioinformatics analysis and multivariate statistics were conducted to identify if specific fecal bacterial assemblages and predicted functions were correlated with urine arsenic concentration. Our results revealed unique assemblages of arsenic volatilizing and pathogenic bacteria positively correlated with increased arsenic concentration in individuals within the two respective communities. Additionally, we observed that commensal gut bacteria negatively correlated with increased arsenic concentration in the two respective communities. Our study has revealed that arsenic poses a broader human health risk than was previously known. It is influential in shaping the gut microbiome through its enrichment of arsenic volatilizing and pathogenic bacteria and subsequent depletion of gut commensals. This aspect of arsenic has the potential to debilitate healthy humans by contributing to disorders like heart and liver cancers and diabetes, and it has already been shown to contribute to serious diseases and disorders, including skin lesions, gangrene and several types of skin, renal, lung, and liver cancers in disadvantaged areas of the world like Nepal.

Acid mine drainage (AMD) is an environmental issue that can be characterized by either acidic or circumneutral pH and high dissolved metal content in contaminated waters. It is estimated to affect roughly 3000 miles of waterways within the state of Pennsylvania, with half being acidic and half being circumneutral. To negate the harmful effects of AMD, ∼300 passive remediation systems have been constructed within the state of Pennsylvania. In this study, we evaluated the microbial community structure and functional capability associated with Middle Branch passive remediation system in central PA. Sediment and water samples were collected from each area within the passive remediation system and its receiving stream. Environmental parameters associated with the remediation system were found to explain a significant amount of variation in microbial community structure. This study revealed shifts in microbial community structure from acidophilic bacteria in raw AMD discharge to a more metabolically diverse set of taxa (i.e., Acidimicrobiales, Rhizobiales, Chthoniobacteraceae ) toward the end of the system. Vertical flow ponds and the aerobic wetland showed strong metabolic capability for sulfur redox environments. These findings are integral to the understanding of designing effective passive remediation systems because it provides insight as to how certain bacteria [sulfate reducing bacteria (SRBs) and sulfur oxidizing bacteria (SOBs)] are potentially contributing to a microbially mediated AMD remediation process. This study further supports previous investigations that demonstrated the effectiveness of SRBs in the process of removing sulfate and heavy metals from contaminated water.

Prosthetic joint infections (PJI) are economically and personally costly, and their incidence has been increasing in the United States. Herein, we compared 16S rRNA amplicon sequencing (16S), shotgun metagenomics (MG) and metatranscriptomics (MT) in identifying pathogens causing PJI. Samples were collected from 30 patients, including 10 patients undergoing revision arthroplasty for infection, 10 patients receiving revision for aseptic failure, and 10 patients undergoing primary total joint arthroplasty. Synovial fluid and peripheral blood samples from the patients were obtained at time of surgery. Analysis revealed distinct microbial communities between primary, aseptic, and infected samples using MG, MT, (PERMANOVA p = 0.001), and 16S sequencing (PERMANOVA p < 0.01). MG and MT had higher concordance with culture (83%) compared to 0% concordance of 16S results. Supervised learning methods revealed MT datasets most clearly differentiated infected, primary, and aseptic sample groups. MT data also revealed more antibiotic resistance genes, with improved concordance results compared to MG. These data suggest that a differential and underlying microbial ecology exists within uninfected and infected joints. This study represents the first application of RNA-based sequencing (MT). Further work on larger cohorts will provide opportunities to employ deep learning approaches to improve accuracy, predictive power, and clinical utility.

As one of the top public health challenges outlined by the Centers for Disease Control (CDC), estimates report that hospital acquired infections (HAIs) claim the lives of 99,000 Americans and cost healthcare providers over $28 billion each year. In addition to underlying conditions related to age, elderly patients in long-term care facilities are at an elevated risk of acquiring HAIs. A large percentage of HAIs is attributable to contaminated surfaces and medical devices. To that end, this study utilized a metatranscriptomic sequencing workflow (CSI-Dx™) to profile active microbial communities from surfaces in the HJ Heinz Community Living Center, a long-term care facility in the Veterans Affairs Pittsburgh Health Care System. Swabs were collected from high-touch surfaces (Keyboard, Ledge, Workstation on Wheels, Worksurfaces) before (Baseline) and after cleanSURFACES® were installed at 4 timepoints (Day 1, Day 7, Day 14, and Day 30). Microbial richness was significantly reduced after cleanSURFACES® intervention (Wilcoxon test with Holm correction, p=0.000179). Beta diversity results revealed distinct clustering between Baseline and Post-intervention samples (Adonis, p&lt;0.001). Reduction in bacterial (Staphylococcus aureus, Staphylococcus epidermidis, Staphylococcus hominis) and fungal (Malassezia restricta, Candida albicans, Candida glabrata, and Candida orthopsilosis) expression of opportunistic pathogens was observed. Additionally, a subset of taxa (Corynebacterium, Cutibacterium acnes, and Ralstonia pickettii) was present in specific Post-intervention timepoints and surface types. This study revealed decreased microbial activity, highlighting the potential for the combinatorial application of cleanSURFACES® and regular decontamination practices to reduce the prevalence of microbes causing HAIs.

Aortic graft infections, albeit rare, pose a significant treatment challenge. Aortic stump disruption, in particular, is a potentially devastating complication. We describe a novel technique of using the parietal peritoneum to bolster a friable aortic stump in a 56-year-old man after an infected aortobifemoral graft was removed. The parietal peritoneal pedicle, in our experience, provides an effective alternative to reinforce an aortic stump when conventional coverage options have been exhausted.

IntroductionDespite routine implementation of cleaning and disinfection practices in clinical healthcare settings, high-touch environmental surfaces and contaminated equipment often serve as reservoirs for the transmission of pathogens associated with healthcare-associated infections (HAIs).MethodsThe current study involved the analysis of high-touch surface swabs using a metatranscriptomic sequencing workflow (CSI-Dx™) to assess the efficacy of cleanSURFACES® technology in decreasing microbial burden by limiting re-contamination. This is a non-human single center study conducted in the Emergency Department (ED) and on an inpatient Oncology Ward of Walter Reed National Military Medical Center that have followed hygienic practices during the COVID-19 pandemic environment.ResultsAlthough there was no difference in observed microbial richness (two-tailed Wilcoxon test with Holm correction, P &gt; 0.05), beta diversity findings identified shifts in microbial community structure between surfaces from baseline and post-intervention timepoints (Day 1, Day 7, Day 14, and Day 28). Biomarker and regression analyses identified significant reductions in annotated transcripts for various clinically relevant microorganisms' post-intervention, coagulase-negative staphylococci and Malassezia restricta, at ED and Oncology ward, respectively. Additionally, post-intervention samples predominantly consisted of Proteobacteria and to a lesser extent skin commensals and endogenous environmental microorganisms in both departments.DiscussionFindings support the value of cleanSURFACES®, when coupled with routine disinfection practices, to effectively impact on the composition of active microbial communities found on high-touch surfaces in two different patient care areas of the hospital (one outpatient and one inpatient) with unique demands and patient-centered practices.